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Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective

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  • Cynthia Pagliaro
  • Dhagash Mehta
  • Han-Tai Shiao
  • Shaofei Wang
  • Luwei Xiong

Abstract

Modeling investor behavior is crucial to identifying behavioral coaching opportunities for financial advisors. With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions. These insights are used to predict investor needs during adverse market conditions; thus allowing advisors to coach investors and help avoid inappropriate financial decision-making. First, we perform topic modeling to gain insight into the emerging topics and trends. Based on this insight, we construct a supervised classification model to predict the probability that an advised investor will require behavioral coaching during volatile market periods. To the best of our knowledge, ours is the first work on exploring the advisor-investor relationship using unstructured data. This work may have far-reaching implications for both traditional and emerging financial advisory service models like robo-advising.

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  • Cynthia Pagliaro & Dhagash Mehta & Han-Tai Shiao & Shaofei Wang & Luwei Xiong, 2021. "Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective," Papers 2107.05592, arXiv.org.
    • Handle: RePEc:arx:papers:2107.05592
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    References listed on IDEAS

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    Cited by:

    1. Athota, Vidya S. & Pereira, Vijay & Hasan, Zahid & Vaz, Daicy & Laker, Benjamin & Reppas, Dimitrios, 2023. "Overcoming financial planners’ cognitive biases through digitalization: A qualitative study," Journal of Business Research, Elsevier, vol. 154(C).
    2. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.

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